Lever Hiring Python API Docs | dltHub

Lever is an applicant tracking and hiring platform that exposes REST APIs for managing job postings, opportunities (candidates/applications), requisitions, and related HR data. The REST API base URL is https://api.lever.co/v1 and All requests require Basic authentication using an API key (API key as username, blank password) or an API key in request headers where applicable..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Lever Hiring data in under 10 minutes.

---

What data can I load from Lever Hiring?

Here are some of the endpoints you can load from Lever Hiring:

---

How do I authenticate with the Lever Hiring API?

Lever API uses API keys (created in the Lever admin Integrations/API settings). Use HTTP Basic auth with the API key as the username and an empty password, or send the API key in an Authorization header as required by some endpoints.

1. Get your credentials

1. Sign in to your Lever account as a Super Admin. 2) Go to Settings > Integrations > API Credentials (or open /settings/integrations?tab=api). 3) Create a new API key (choose Postings API or Full API scope as needed and grant confidential data access if required). 4) Copy the generated API key and store it securely.

2. Add them to .dlt/secrets.toml

Copy
[sources.lever_hiring_source]
api_key = "your_lever_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Lever Hiring API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python lever_hiring_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline lever_hiring_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lever_hiring_data
The duckdb destination used duckdb:/lever_hiring.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline lever_hiring_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads postings and opportunities from the Lever Hiring API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def lever_hiring_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.lever.co/v1",
            "auth": {
                "type": "http_basic",
                "username": api_key,
            },
        },
        "resources": [
            {"name": "postings", "endpoint": {"path": "postings"}},
            {"name": "opportunities", "endpoint": {"path": "opportunities", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lever_hiring_pipeline",
        destination="duckdb",
        dataset_name="lever_hiring_data",
    )
    load_info = pipeline.run(lever_hiring_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("lever_hiring_pipeline").dataset()
sessions_df = data.postings.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM lever_hiring_data.postings LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("lever_hiring_pipeline").dataset()
data.postings.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Lever Hiring data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify you're using a valid API key (Super Admin-created key) and that you're sending it via HTTP Basic auth (API key as username, blank password) or the expected header. Ensure the key has permission to access confidential data if you are requesting confidential objects.

Rate limiting and 429 responses

Application-create and other write endpoints are rate limited. Handle 429 responses by implementing exponential backoff and retry. For high-volume operations, batch or throttle requests.

Pagination quirks

The Postings (postings API) use skip and limit query parameters (e.g. ?skip=0&limit=50 or ?mode=json) for paging. Other v1 endpoints commonly support limit/offset or return payloads with data arrays and filters such as created_at_start/created_at_end; some list endpoints include hasNext booleans—check the specific endpoint's docs and use the documented parameters for reliable pagination.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack